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Trading Volume and Arbitrage

Author

Listed:
  • Serge Darolles

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Gaëlle Le Fol

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

Decomposing returns into market and stock specific components is common practice and forms the basis of popular asset pricing models. What about volume? Can volume be decomposed in the same way as returns? Lo and Wang (2000) suggest such a decomposition. Our paper contributes to this literature in two different ways. First, we provide a model to explain why volumes deviate from the benchmark. Our interpretation is in terms of arbitrage strategies and liquidity. Second, we propose a new efficient screening tool that allows practitioners to extract specific information from volume time series. We provide an empirical illustration of the relevance and the possible uses of our approach on daily data from the FTSE index from 2000 to 2002.

Suggested Citation

  • Serge Darolles & Gaëlle Le Fol, 2014. "Trading Volume and Arbitrage," Post-Print hal-01632848, HAL.
    • Handle: RePEc:hal:journl:hal-01632848
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Bialkowski, Jedrzej & Darolles, Serge & Le Fol, Gaëlle, 2008. "Improving VWAP strategies: A dynamic volume approach," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1709-1722, September.
    2. Darolles, Serge & Fol, Gaëlle Le & Mero, Gulten, 2015. "Measuring the liquidity part of volume," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 92-105.
    3. Francesco Calvori & Fabrizio Cipollini & Giampiero M. Gallo, 2014. "Go with the Flow: A GAS model for Predicting Intra-daily Volume Shares," Econometrics Working Papers Archive 2014_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    4. Darolles, Serge & Le Fol, Gaëlle & Mero, Gulten, 2017. "Mixture of distribution hypothesis: Analyzing daily liquidity frictions and information flows," Journal of Econometrics, Elsevier, vol. 201(2), pages 367-383.
    5. Roman Huptas, 2019. "Point forecasting of intraday volume using Bayesian autoregressive conditional volume models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(4), pages 293-310, July.
    6. Serge Darolles & Gaëlle Le Fol, 2004. "Nouvelles techniques de gestion et leur impact sur la volatilité," Revue d'Économie Financière, Programme National Persée, vol. 74(1), pages 231-243.
    7. Machado, André & Lima, Fabiano Guasti, 2021. "Sell-side analyst reports and decision-maker reactions: Role of heuristics," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    8. Staer, Arsenio & Sottile, Pedro, 2018. "Equivalent volume and comovement," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 143-157.
    9. Jedrzej Bialkowski & Serge Darolles & Gaëlle Le Fol, 2005. "Decomposing Volume for VWAP Strategies," Working Papers 2005-16, Center for Research in Economics and Statistics.

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